CHURN PREDICTION CASE STUDY
  1. Data Overview 1.1 Print no. of rows and columns of the dataset 1.2 Print all the features as a list 1.3 Print number of missing values for dataset 1.4 Print number of unique values for each feature.

  2. Data Manipulation 2.1 Replace empty spaces with NaN in "total charges" column 2.2 Drop null values from "total charges" column which contains .15% missing data 2.3 Convert "total charges" to float. 2.4 Replace "No Internet Service" to No or folowwing columns

      "Online Security","Online Backup","Device Protection","TechSupport","Streaming TV","Streaming Movies"

    2.5 Replace "Senior Citizen" values {1:Yes,0:No} 2.6 Create a function "def Tenure_lab" to convert tenure to categories as follows.

         <= 12 -> "Tenure_0-12"
     > 12, <24 -> "Tenure_12-24"
     > 24 ,  <=48 -> "Tenure_24-48"
     >48, <=60 -> "Tenure_48-60"
     > 60 -> "Tenure_gt_60"

     def Tenure_lab(tenure): -- pass the dataframe columns
         if df['tenure'].value <=20:
             return "Tenure_0-12"  df['tenure'].value = "Tenure_0-12"
         elseif tenure -gt 12 && tenure  -lt 24:
             return "Tenure_12-24"
         elseif tenure -gt 24 && tenure <= 48:
             return "Tenure_24-48"
         elseif tenure -gt 48 && tenure <= 60:
             return "Tenure_48-60"
         else:
             return "Tenure_gt-60"

    2.7 Seperate churn and not churn customers into variables churn, not_churn. 2.8 Seperate categorical and numerical columns into variables cat_cols,cat_num.

  3. Exploratory Data Analysis Contruct the following functions: 3.1 def plot_pie(column) 
      Description : Using all categorical columns, contruct a pie chart for churn and non churn customers
    3.2 def histogram(column) 
      Description : Using all numerical columns, make histogram to compare churn and non churn customers.
    3.3 Create a function called "def scatter_matrix(df)" 
      Description : For all numeric columns construct scatterplot which can visualize churn and non churn customer          in one graph.
    3.4 For all different trnure groups build a barplot to visualize churn and non churn customers. 3.5 Create a scatter plot betwen "Total Charges" and "Monthly Charges" and color code the data points using tenure groups. 3.6 Repeat above, color code to churn and not churn. 3.7 Construct barplot for Total Charges and Monthly Charges seperately with tenure group on x-axis and bar should be depicting churn and not churn for each customer tenure group. 3.8 Construct a correlation heatmap for all numerical variables.
  4. Data Preprocessing 4.1 Seperate CustomerID column in ID_col 4.2 Seperate churn in target_col 4.3 Seperate categorical columns in cat_cols 4.4 Seperate numerical columns in num_cols 4.5 Seperate binary columns with 2 values & store in bin_cols 4.6 Seperate variables with multiple values and store in multi_cols. 4.7 Label encode binary columns using LabelEncoder() 4.8 Dummy encode multi_cols using pandas 4.9 Scale numeric columns using StandardScalar() 4.10 Drop Original values and Merge the scaled values with your dataframe.
  5. Model Building 5.1 Build Logistic Regression 5.2 Build Decision Trees 5.3 Build Random Forest 5.4 For each model print accuracy, precision and recall 5.5 Draw ROC curve for each model 5.6 Compare model performance

MAIL TO : loveesh.imarticus@gmail.com SUBJECT LINE : FirstName_MLP3 FILES : "html","ipynb"

In [1]:
# Import all the libraries required

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report,precision_score,recall_score, roc_curve, roc_auc_score, confusion_matrix,auc
from time import time
from scipy.stats import randint as sp_randint
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from yellowbrick.classifier import DiscriminationThreshold
import plotly.figure_factory as ff
import plotly.graph_objs as go
import plotly.offline as py
import warnings
warnings.filterwarnings('ignore')
In [2]:
# Setting for full column selection

pd.set_option('display.max_columns', 999)
In [3]:
# Read the " churn" dataset

df_churn = pd.read_csv('/Users/abhisheksingh/Desktop/MLP/churn.csv', na_filter=False)
In [4]:
# Display top 5 records

df_churn.head()
Out[4]:
customerID gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn
0 7590-VHVEG Female 0 Yes No 1 No No phone service DSL No Yes No No No No Month-to-month Yes Electronic check 29.85 29.85 No
1 5575-GNVDE Male 0 No No 34 Yes No DSL Yes No Yes No No No One year No Mailed check 56.95 1889.5 No
2 3668-QPYBK Male 0 No No 2 Yes No DSL Yes Yes No No No No Month-to-month Yes Mailed check 53.85 108.15 Yes
3 7795-CFOCW Male 0 No No 45 No No phone service DSL Yes No Yes Yes No No One year No Bank transfer (automatic) 42.30 1840.75 No
4 9237-HQITU Female 0 No No 2 Yes No Fiber optic No No No No No No Month-to-month Yes Electronic check 70.70 151.65 Yes
In [5]:
# Print the number of rows and columns

print('Rows : ', df_churn.shape[0])
print('Columns : ', df_churn.shape[1])

Rows :  7043
Columns :  21
In [6]:
# Print all the features as a list

df_churn.columns.to_list()
Out[6]:
['customerID',
 'gender',
 'SeniorCitizen',
 'Partner',
 'Dependents',
 'tenure',
 'PhoneService',
 'MultipleLines',
 'InternetService',
 'OnlineSecurity',
 'OnlineBackup',
 'DeviceProtection',
 'TechSupport',
 'StreamingTV',
 'StreamingMovies',
 'Contract',
 'PaperlessBilling',
 'PaymentMethod',
 'MonthlyCharges',
 'TotalCharges',
 'Churn']
In [7]:
# Replace empty spaces with Nan

df = df_churn.replace(' ',np.NaN)
In [8]:
#  Getting the null values in a variable

missing_data = df.isnull()
In [9]:
# Printing the value counts for all the columns

for column in missing_data.columns.values.tolist(): # Convert into list to iterate through columns
    print(column)
    print(missing_data[column].value_counts())  # Count True and False column wise
    
    print("")

customerID
False    7043
Name: customerID, dtype: int64

gender
False    7043
Name: gender, dtype: int64

SeniorCitizen
False    7043
Name: SeniorCitizen, dtype: int64

Partner
False    7043
Name: Partner, dtype: int64

Dependents
False    7043
Name: Dependents, dtype: int64

tenure
False    7043
Name: tenure, dtype: int64

PhoneService
False    7043
Name: PhoneService, dtype: int64

MultipleLines
False    7043
Name: MultipleLines, dtype: int64

InternetService
False    7043
Name: InternetService, dtype: int64

OnlineSecurity
False    7043
Name: OnlineSecurity, dtype: int64

OnlineBackup
False    7043
Name: OnlineBackup, dtype: int64

DeviceProtection
False    7043
Name: DeviceProtection, dtype: int64

TechSupport
False    7043
Name: TechSupport, dtype: int64

StreamingTV
False    7043
Name: StreamingTV, dtype: int64

StreamingMovies
False    7043
Name: StreamingMovies, dtype: int64

Contract
False    7043
Name: Contract, dtype: int64

PaperlessBilling
False    7043
Name: PaperlessBilling, dtype: int64

PaymentMethod
False    7043
Name: PaymentMethod, dtype: int64

MonthlyCharges
False    7043
Name: MonthlyCharges, dtype: int64

TotalCharges
False    7032
True       11
Name: TotalCharges, dtype: int64

Churn
False    7043
Name: Churn, dtype: int64
In [10]:
# Describe the data which includes Unique values for each feature(Categorical) 

print ("\nUnique values :  \n",df.nunique())

Unique values :  
 customerID          7043
gender                 2
SeniorCitizen          2
Partner                2
Dependents             2
tenure                73
PhoneService           2
MultipleLines          3
InternetService        3
OnlineSecurity         3
OnlineBackup           3
DeviceProtection       3
TechSupport            3
StreamingTV            3
StreamingMovies        3
Contract               3
PaperlessBilling       2
PaymentMethod          4
MonthlyCharges      1585
TotalCharges        6530
Churn                  2
dtype: int64
In [11]:
# Drop entire ros with null values

df.dropna(axis=0, inplace=True)
In [12]:
# Check the missing value counts 

df.isnull().sum()
Out[12]:
customerID          0
gender              0
SeniorCitizen       0
Partner             0
Dependents          0
tenure              0
PhoneService        0
MultipleLines       0
InternetService     0
OnlineSecurity      0
OnlineBackup        0
DeviceProtection    0
TechSupport         0
StreamingTV         0
StreamingMovies     0
Contract            0
PaperlessBilling    0
PaymentMethod       0
MonthlyCharges      0
TotalCharges        0
Churn               0
dtype: int64
In [13]:
# Convert the datatype of "TotalCharges" column to "float".

df['TotalCharges'] = df['TotalCharges'].astype('float32')
In [14]:
# Check the datatype of the Column "TotalCharges"

df['TotalCharges'].dtype
Out[14]:
dtype('float32')
In [15]:
#Replace "No Internet Service" to No or following columns
 #        "Online Security","Online Backup","Device Protection","TechSupport","Streaming TV","Streaming Movies"

cols = ['OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport','StreamingTV','StreamingMovies']
df[cols]=df[cols].replace('No internet service','No')
In [16]:
df = df.reset_index()[df.columns]
In [17]:
# Double check for one column

df['OnlineSecurity'].value_counts()
Out[17]:
No     5017
Yes    2015
Name: OnlineSecurity, dtype: int64
In [18]:
# Replace "Senior Citizen" values {1:Yes,0:No}

df['SeniorCitizen'] = df['SeniorCitizen'].replace({1:'Yes',0:'No'})
In [19]:
# Check the "Senior Citizen" column values

df['SeniorCitizen'].value_counts()
Out[19]:
No     5890
Yes    1142
Name: SeniorCitizen, dtype: int64
In [20]:
# Create a function "def Tenure_lab" to convert tenure to categories.

def Tenure_lab(df) :
    
    if df['tenure'] <= 12 :
        return "Tenure_0-12"
    elif (df['tenure'] > 12) & (df['tenure'] <= 24 ):
        return "Tenure_12-24"
    elif (df['tenure'] > 24) & (df['tenure'] <= 48):
        return "Tenure_24-48"
    elif (df['tenure'] > 48) & (df['tenure'] <= 60):
        return "Tenure_48-60"
    elif df['tenure'] > 60:
        return "Tenure_gt-60"
In [21]:
# Call the above function

df['tenure_cat'] = df.apply(lambda df:Tenure_lab(df),axis = 1)
In [22]:
# Check the unique values in the newly created columns

df['tenure_cat'].unique()
Out[22]:
array(['Tenure_0-12', 'Tenure_24-48', 'Tenure_12-24', 'Tenure_gt-60',
       'Tenure_48-60'], dtype=object)
In [23]:
# Seperate churn and not churn customers into variables churn, not_churn

churn     = df[df['Churn'] == "Yes"]
not_churn = df[df['Churn'] == "No"]
In [24]:
churn.head()
Out[24]:
customerID gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn tenure_cat
2 3668-QPYBK Male No No No 2 Yes No DSL Yes Yes No No No No Month-to-month Yes Mailed check 53.85 108.150002 Yes Tenure_0-12
4 9237-HQITU Female No No No 2 Yes No Fiber optic No No No No No No Month-to-month Yes Electronic check 70.70 151.649994 Yes Tenure_0-12
5 9305-CDSKC Female No No No 8 Yes Yes Fiber optic No No Yes No Yes Yes Month-to-month Yes Electronic check 99.65 820.500000 Yes Tenure_0-12
8 7892-POOKP Female No Yes No 28 Yes Yes Fiber optic No No Yes Yes Yes Yes Month-to-month Yes Electronic check 104.80 3046.050049 Yes Tenure_24-48
13 0280-XJGEX Male No No No 49 Yes Yes Fiber optic No Yes Yes No Yes Yes Month-to-month Yes Bank transfer (automatic) 103.70 5036.299805 Yes Tenure_48-60
In [25]:
not_churn.head()
Out[25]:
customerID gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn tenure_cat
0 7590-VHVEG Female No Yes No 1 No No phone service DSL No Yes No No No No Month-to-month Yes Electronic check 29.85 29.850000 No Tenure_0-12
1 5575-GNVDE Male No No No 34 Yes No DSL Yes No Yes No No No One year No Mailed check 56.95 1889.500000 No Tenure_24-48
3 7795-CFOCW Male No No No 45 No No phone service DSL Yes No Yes Yes No No One year No Bank transfer (automatic) 42.30 1840.750000 No Tenure_24-48
6 1452-KIOVK Male No No Yes 22 Yes Yes Fiber optic No Yes No No Yes No Month-to-month Yes Credit card (automatic) 89.10 1949.400024 No Tenure_12-24
7 6713-OKOMC Female No No No 10 No No phone service DSL Yes No No No No No Month-to-month No Mailed check 29.75 301.899994 No Tenure_0-12
In [26]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7032 entries, 0 to 7031
Data columns (total 22 columns):
customerID          7032 non-null object
gender              7032 non-null object
SeniorCitizen       7032 non-null object
Partner             7032 non-null object
Dependents          7032 non-null object
tenure              7032 non-null int64
PhoneService        7032 non-null object
MultipleLines       7032 non-null object
InternetService     7032 non-null object
OnlineSecurity      7032 non-null object
OnlineBackup        7032 non-null object
DeviceProtection    7032 non-null object
TechSupport         7032 non-null object
StreamingTV         7032 non-null object
StreamingMovies     7032 non-null object
Contract            7032 non-null object
PaperlessBilling    7032 non-null object
PaymentMethod       7032 non-null object
MonthlyCharges      7032 non-null float64
TotalCharges        7032 non-null float32
Churn               7032 non-null object
tenure_cat          7032 non-null object
dtypes: float32(1), float64(1), int64(1), object(19)
memory usage: 1.2+ MB
In [27]:
# Seperate categorical and numerical columns into variables at_cols,cat_num

Id_col = 'customerID'
target_col = 'Churn'
cat_cols = df.select_dtypes(include=['object']).columns.tolist()
cat_cols = [cols for cols in cat_cols if cols not in target_col]

num_cols   = df.select_dtypes(exclude=['object']).columns.tolist()
In [28]:
cat_cols
Out[28]:
['customerID',
 'gender',
 'SeniorCitizen',
 'Partner',
 'Dependents',
 'PhoneService',
 'MultipleLines',
 'InternetService',
 'OnlineSecurity',
 'OnlineBackup',
 'DeviceProtection',
 'TechSupport',
 'StreamingTV',
 'StreamingMovies',
 'Contract',
 'PaperlessBilling',
 'PaymentMethod',
 'tenure_cat']
In [29]:
num_cols
Out[29]:
['tenure', 'MonthlyCharges', 'TotalCharges']
In [30]:
# Exploratory Data Analysis
    
#Contruct the following functions:
In [31]:
# def plot_pie(column)
#Description : Using all categorical columns, contruct a pie chart for churn and non churn customers
In [32]:
#?go.Layout
In [33]:
#?go.Pie
In [34]:
def plot_pie(column):
    Pie1=go.Pie(labels=churn[column].value_counts().keys().tolist(),
                values=churn[column].value_counts().values.tolist(),
                name    = "Churn Customers",
                domain  = dict(x = [0,.48]),
                    marker  = dict(line = dict(width = 2,
                                               color = "rgb(243,243,243)")
                                  ),hole=.4)
    Pie2=go.Pie(labels=not_churn[column].value_counts().keys().tolist(),
                values=not_churn[column].value_counts().values.tolist(),
                name    = "Not Churn Customers",
                domain  = dict(x = [.52,1]),
                    marker  = dict(line = dict(width = 2,
                                               color = "rgb(243,243,243)")
                                  ),hole=.4)

    
    layout = go.Layout(dict(title = column,
                            plot_bgcolor  = "rgb(200,200,200)",
                            paper_bgcolor = "rgb(250,250,250)",
                            annotations = [dict(text = "churn customers",
                                                font = dict(size = 10),
                                               showarrow = False,
                                                x = .18, y = .5),
                                           dict(text = "Non churn customers",
                                                font = dict(size = 10),
                                               showarrow = False,
                                                x = .83,y = .5
                                        
                                               )
                                          ]
                           )
                      )

    fig = go.Figure(data = [Pie1,Pie2], layout = layout)
    py.iplot(fig)
for i in [x for x in cat_cols if x!='customerID']:
    plot_pie(i)
In [35]:
#?go.Histogram
In [36]:
# def histogram(column)
# Description : Using all numerical columns, make histogram to compare churn and non churn customers.

def histogram(column) :
    hist1 = go.Histogram(x  = churn[column],
                          histnorm= "percent",
                          name = "Churn Customers",
                         opacity = .8 
                         ) 
    
    hist2 = go.Histogram(x  = not_churn[column],
                          histnorm= "percent",
                          name = "Non Churn Customers",
                         opacity = .8 
                         ) 
    
    layout = go.Layout(dict(title =column,
                            plot_bgcolor  = "rgb(200,200,200)",
                            paper_bgcolor = "rgb(250,250,250)",
                            xaxis = dict(gridcolor = 'rgb(255, 255, 255)',
                                             title = column,
                                            ),
                            yaxis = dict(gridcolor = 'rgb(255, 255, 255)',
                                             title = "percent",
                                            ),
                           )
                      )
    fig  = go.Figure(data=[hist1,hist2],layout=layout)
    
    py.iplot(fig)
    
for i in num_cols:
    histogram(i)
In [37]:
# Create a function called "def scatter_matrix(df)"
# Description : For all numeric columns construct scatterplot which can visualize churn and 
# non churn customer in one graph.

pd.plotting.scatter_matrix(df[num_cols], alpha=0.2, figsize=(10, 10), diagonal='kde')
plt.show()
In [38]:
sns.pairplot(df[num_cols])
plt.show()
In [39]:
# For all different tenure groups build a barplot to visualize churn and non churn customers.

bar_ch  =  churn['tenure_cat'].value_counts().reset_index()
bar_ch.columns  = ['tenure_cat','count']

bar_nt_ch  =  not_churn['tenure_cat'].value_counts().reset_index()
bar_nt_ch.columns  = ['tenure_cat','count']
In [40]:
bar_ch
Out[40]:
tenure_cat count
0 Tenure_0-12 1037
1 Tenure_24-48 325
2 Tenure_12-24 294
3 Tenure_48-60 120
4 Tenure_gt-60 93
In [41]:
bar_ch.columns
Out[41]:
Index(['tenure_cat', 'count'], dtype='object')
In [42]:
#bar - churn
bar1 = go.Bar(x = bar_ch['tenure_cat']  , y = bar_ch['count'],
                name = 'Churn Customers',
                opacity = .9)

#bar - not churn
bar2 = go.Bar(x = bar_nt_ch['tenure_cat'] , y = bar_nt_ch['count'],
                name = 'Non Churn Customers',
                opacity = .9)

layout = go.Layout(dict(title = "Churn in tenure groups",
                        plot_bgcolor  = "rgb(243,243,243)",
                        paper_bgcolor = "rgb(243,243,243)",
                        xaxis = dict(gridcolor = 'rgb(255, 255, 255)',
                                     title = 'Tenure Category'),
                        yaxis = dict(gridcolor = 'rgb(255, 255, 255)',
                                     title = 'count'),
                       )
                  )
                        

fig  = go.Figure(data=[bar1,bar2],layout=layout)
py.iplot(fig)
In [43]:
#?sns.scatterplot
In [44]:
#ax = sns.scatterplot(x=churn['tenure_cat'], y=df[num_cols])
In [45]:
# Create a scatter plot betwen "Total Charges" and "Monthly Charges" and color code 
# the data points using tenure categories.

df[['MonthlyCharges', 'TotalCharges','tenure',"tenure_cat"]]

# Scatter plot monthly charges & total charges by tenure categories
scatter1 = []
scatter_plot = {'Tenure_0-12' : 'red', 'Tenure_12-24': 'green','Tenure_24-48':'purple',
                'Tenure_48-60':'yellow','Tenure_gt-60':'black'}
for keys,values in scatter_plot.items():
     scatter1.append(go.Scatter(x = df[df['tenure_cat'] == keys]['MonthlyCharges'],
                        y = df[df['tenure_cat'] == keys]['TotalCharges'],
                                          mode = "markers",marker = dict(line = dict(color = "black",
                                                                   width = .2),
                                                       size = 4 , color = values,
                                                       symbol = "diamond-dot",
                                                      ),
                        name = keys
                                          ,
                        opacity = .9
                       ))

#layout
def layout_title(title) :
    layout = go.Layout(dict(title = title,
                            plot_bgcolor  = "rgb(243,243,243)",
                            paper_bgcolor = "rgb(243,243,243)",
                            xaxis = dict(gridcolor = 'rgb(255, 255, 255)',
                                         title = "Monthly charges",
                                         ),
                            yaxis = dict(gridcolor = 'rgb(255, 255, 255)',
                                         title = "Total Charges",
                                         ),
                            height = 600
                           )
                      )
    return layout

layout1  = layout_title("Monthly Charges & Total Charges by Tenure Categories")
fig1 = go.Figure(data = scatter1,layout = layout1)
py.iplot(fig1)
In [46]:
df
Out[46]:
customerID gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn tenure_cat
0 7590-VHVEG Female No Yes No 1 No No phone service DSL No Yes No No No No Month-to-month Yes Electronic check 29.85 29.850000 No Tenure_0-12
1 5575-GNVDE Male No No No 34 Yes No DSL Yes No Yes No No No One year No Mailed check 56.95 1889.500000 No Tenure_24-48
2 3668-QPYBK Male No No No 2 Yes No DSL Yes Yes No No No No Month-to-month Yes Mailed check 53.85 108.150002 Yes Tenure_0-12
3 7795-CFOCW Male No No No 45 No No phone service DSL Yes No Yes Yes No No One year No Bank transfer (automatic) 42.30 1840.750000 No Tenure_24-48
4 9237-HQITU Female No No No 2 Yes No Fiber optic No No No No No No Month-to-month Yes Electronic check 70.70 151.649994 Yes Tenure_0-12
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
7027 6840-RESVB Male No Yes Yes 24 Yes Yes DSL Yes No Yes Yes Yes Yes One year Yes Mailed check 84.80 1990.500000 No Tenure_12-24
7028 2234-XADUH Female No Yes Yes 72 Yes Yes Fiber optic No Yes Yes No Yes Yes One year Yes Credit card (automatic) 103.20 7362.899902 No Tenure_gt-60
7029 4801-JZAZL Female No Yes Yes 11 No No phone service DSL Yes No No No No No Month-to-month Yes Electronic check 29.60 346.450012 No Tenure_0-12
7030 8361-LTMKD Male Yes Yes No 4 Yes Yes Fiber optic No No No No No No Month-to-month Yes Mailed check 74.40 306.600006 Yes Tenure_0-12
7031 3186-AJIEK Male No No No 66 Yes No Fiber optic Yes No Yes Yes Yes Yes Two year Yes Bank transfer (automatic) 105.65 6844.500000 No Tenure_gt-60

7032 rows × 22 columns

In [47]:
# Repeat above, color code to churn and not churn.

churn1=[]
churn_plot={'Yes':'red', 'No':'blue'}
for keys,values in churn_plot.items():
    churn1.append(go.Scatter(x = df[df['Churn'] == keys]['MonthlyCharges'],
                        y = df[df['Churn'] == keys]['TotalCharges'],
                        mode = 'markers',marker = dict(line = dict(color = 'black',
                                                                   width = .2),
                                                       size = 4 , color = values,
                                                       symbol = 'diamond-dot',
                                                      ),
                        name = 'Churn - ' + keys,
                        opacity = .9
                       ))

layout2  = layout_title("Monthly Charges & Total Charges by Churn and not churn")
fig2 = go.Figure(data = churn1,layout = layout2)
py.iplot(fig2)
In [48]:
# Construct barplot for Total Charges and Monthly Charges seperately with tenure group 
# on x-axis and bar should be depicting churn and not churn for each customer tenure group.
In [49]:
tenure_category = df[['tenure_cat','Churn','MonthlyCharges','TotalCharges']]
tenure_category[tenure_category['Churn']=='Yes']['tenure_cat']
Out[49]:
2        Tenure_0-12
4        Tenure_0-12
5        Tenure_0-12
8       Tenure_24-48
13      Tenure_48-60
            ...     
7010     Tenure_0-12
7015     Tenure_0-12
7021     Tenure_0-12
7023    Tenure_gt-60
7030     Tenure_0-12
Name: tenure_cat, Length: 1869, dtype: object
In [51]:
tenure_category = df[['tenure_cat','Churn','MonthlyCharges','TotalCharges']]
churn_cat = {'Yes':'red','No':'blue'}
column = df[['MonthlyCharges','TotalCharges']].columns.to_list()
barplot1_M = []
barplot1_T = []

for i,j in churn_cat.items():
    barplot1_M.append(go.Bar(x = tenure_category.loc[tenure_category['Churn'] == i]['tenure_cat'],
                    y = tenure_category.loc[tenure_category['Churn'] == i][column[0]],
                            name=i, marker = dict(line = dict(width = 1,color=j))))
    barplot1_T.append(go.Bar(x = tenure_category.loc[tenure_category['Churn'] == i]['tenure_cat'],
                    y = tenure_category.loc[tenure_category['Churn'] == i][column[1]],
                             name=i,marker = dict(line = dict(width = 1,color=j))
                   
                   ))
    

#function for layout
def layout_plot(title,xaxis_lab,yaxis_lab) :
    layout = go.Layout(dict(title = title,
                            plot_bgcolor  = "rgb(243,243,243)",
                            paper_bgcolor = "rgb(243,243,243)",
                            xaxis = dict(gridcolor = 'rgb(255, 255, 255)',title = xaxis_lab,
                                         zerolinewidth=1,ticklen=5,gridwidth=2),
                            yaxis = dict(gridcolor = 'rgb(255, 255, 255)',title = yaxis_lab,
                                         zerolinewidth=1,ticklen=5,gridwidth=2),
                           )
                      )
    return layout



layout1 = layout_plot('Monthly Charges by Tenure Category',
                      'Tenure Category','Monthly Charges')
fig1    = go.Figure(data=barplot1_M,layout=layout1)

layout2 = layout_plot('Total Charges by Tenure Category',
                      'Tenure Category','Total Charges')
fig2    = go.Figure(data=barplot1_T,layout=layout2)

py.iplot(fig1)
py.iplot(fig2)
In [52]:
cols = df[num_cols].corr().columns.to_list()
cols
Out[52]:
['tenure', 'MonthlyCharges', 'TotalCharges']
In [53]:
# Construct a correlation heatmap for all numerical variables

cols = df[num_cols].corr().columns.to_list()

corr_array  = np.array(df[num_cols].corr())

#Plotting
heat_map = go.Heatmap(z = corr_array,
                   x = cols,
                   y = cols,
                   colorscale = "Viridis",
                   colorbar   = dict(title = "Correlation",
                                     titleside = "right"
                                    ) ,
                  )

layout = go.Layout(dict(title = "Correlation Matrix for Numeric variables",
                        autosize = False,
                        height  = 720,
                        width   = 800
                       )
                  )


fig = go.Figure(data=heat_map,layout=layout)
py.iplot(fig)
In [54]:
# Data Preprocessing
In [55]:
# Seperate CustomerID column in ID_col
Id_col
Out[55]:
'customerID'
In [56]:
# Seperate churn in target_col

target_col
Out[56]:
'Churn'
In [57]:
# Seperate categorical columns in cat_cols

cat_cols
Out[57]:
['customerID',
 'gender',
 'SeniorCitizen',
 'Partner',
 'Dependents',
 'PhoneService',
 'MultipleLines',
 'InternetService',
 'OnlineSecurity',
 'OnlineBackup',
 'DeviceProtection',
 'TechSupport',
 'StreamingTV',
 'StreamingMovies',
 'Contract',
 'PaperlessBilling',
 'PaymentMethod',
 'tenure_cat']
In [58]:
# Seperate numerical columns in num_cols

num_cols
Out[58]:
['tenure', 'MonthlyCharges', 'TotalCharges']
In [59]:
df.nunique()
Out[59]:
customerID          7032
gender                 2
SeniorCitizen          2
Partner                2
Dependents             2
tenure                72
PhoneService           2
MultipleLines          3
InternetService        3
OnlineSecurity         2
OnlineBackup           2
DeviceProtection       2
TechSupport            2
StreamingTV            2
StreamingMovies        2
Contract               3
PaperlessBilling       2
PaymentMethod          4
MonthlyCharges      1584
TotalCharges        6530
Churn                  2
tenure_cat             5
dtype: int64
In [60]:
# Seperate binary columns with 2 values & store in bin_cols

#df.nunique()
bin_cols = df.nunique()[df.nunique()==2].keys().to_list()
In [61]:
# Seperate variables with multiple values and store in multi_cols
col_req = [x for x in df.columns if x!='customerID']
multi_cols = df[col_req].nunique()[df[col_req].nunique()>2].keys().to_list()
In [62]:
multi_cols
Out[62]:
['tenure',
 'MultipleLines',
 'InternetService',
 'Contract',
 'PaymentMethod',
 'MonthlyCharges',
 'TotalCharges',
 'tenure_cat']
In [63]:
df[multi_cols]
Out[63]:
tenure MultipleLines InternetService Contract PaymentMethod MonthlyCharges TotalCharges tenure_cat
0 1 No phone service DSL Month-to-month Electronic check 29.85 29.850000 Tenure_0-12
1 34 No DSL One year Mailed check 56.95 1889.500000 Tenure_24-48
2 2 No DSL Month-to-month Mailed check 53.85 108.150002 Tenure_0-12
3 45 No phone service DSL One year Bank transfer (automatic) 42.30 1840.750000 Tenure_24-48
4 2 No Fiber optic Month-to-month Electronic check 70.70 151.649994 Tenure_0-12
... ... ... ... ... ... ... ... ...
7027 24 Yes DSL One year Mailed check 84.80 1990.500000 Tenure_12-24
7028 72 Yes Fiber optic One year Credit card (automatic) 103.20 7362.899902 Tenure_gt-60
7029 11 No phone service DSL Month-to-month Electronic check 29.60 346.450012 Tenure_0-12
7030 4 Yes Fiber optic Month-to-month Mailed check 74.40 306.600006 Tenure_0-12
7031 66 No Fiber optic Two year Bank transfer (automatic) 105.65 6844.500000 Tenure_gt-60

7032 rows × 8 columns

In [64]:
# Label encode binary columns using LabelEncoder()

bin_col1  = df[bin_cols].apply(LabelEncoder().fit_transform)
In [65]:
bin_col1
Out[65]:
gender SeniorCitizen Partner Dependents PhoneService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies PaperlessBilling Churn
0 0 0 1 0 0 0 1 0 0 0 0 1 0
1 1 0 0 0 1 1 0 1 0 0 0 0 0
2 1 0 0 0 1 1 1 0 0 0 0 1 1
3 1 0 0 0 0 1 0 1 1 0 0 0 0
4 0 0 0 0 1 0 0 0 0 0 0 1 1
... ... ... ... ... ... ... ... ... ... ... ... ... ...
7027 1 0 1 1 1 1 0 1 1 1 1 1 0
7028 0 0 1 1 1 0 1 1 0 1 1 1 0
7029 0 0 1 1 0 1 0 0 0 0 0 1 0
7030 1 1 1 0 1 0 0 0 0 0 0 1 1
7031 1 0 0 0 1 1 0 1 1 1 1 1 0

7032 rows × 13 columns

In [66]:
# Dummy encode multi_cols using pandas

df_1 = pd.get_dummies(df[multi_cols])
In [67]:
bin_col1
Out[67]:
gender SeniorCitizen Partner Dependents PhoneService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies PaperlessBilling Churn
0 0 0 1 0 0 0 1 0 0 0 0 1 0
1 1 0 0 0 1 1 0 1 0 0 0 0 0
2 1 0 0 0 1 1 1 0 0 0 0 1 1
3 1 0 0 0 0 1 0 1 1 0 0 0 0
4 0 0 0 0 1 0 0 0 0 0 0 1 1
... ... ... ... ... ... ... ... ... ... ... ... ... ...
7027 1 0 1 1 1 1 0 1 1 1 1 1 0
7028 0 0 1 1 1 0 1 1 0 1 1 1 0
7029 0 0 1 1 0 1 0 0 0 0 0 1 0
7030 1 1 1 0 1 0 0 0 0 0 0 1 1
7031 1 0 0 0 1 1 0 1 1 1 1 1 0

7032 rows × 13 columns

In [68]:
df_1
Out[68]:
tenure MonthlyCharges TotalCharges MultipleLines_No MultipleLines_No phone service MultipleLines_Yes InternetService_DSL InternetService_Fiber optic InternetService_No Contract_Month-to-month Contract_One year Contract_Two year PaymentMethod_Bank transfer (automatic) PaymentMethod_Credit card (automatic) PaymentMethod_Electronic check PaymentMethod_Mailed check tenure_cat_Tenure_0-12 tenure_cat_Tenure_12-24 tenure_cat_Tenure_24-48 tenure_cat_Tenure_48-60 tenure_cat_Tenure_gt-60
0 1 29.85 29.850000 0 1 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 0
1 34 56.95 1889.500000 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0
2 2 53.85 108.150002 1 0 0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 0
3 45 42.30 1840.750000 0 1 0 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0
4 2 70.70 151.649994 1 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
7027 24 84.80 1990.500000 0 0 1 1 0 0 0 1 0 0 0 0 1 0 1 0 0 0
7028 72 103.20 7362.899902 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1
7029 11 29.60 346.450012 0 1 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 0
7030 4 74.40 306.600006 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0
7031 66 105.65 6844.500000 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1

7032 rows × 21 columns

In [69]:
df_1 = pd.concat([df_1, bin_col1], axis=1)
In [70]:
df_1
Out[70]:
tenure MonthlyCharges TotalCharges MultipleLines_No MultipleLines_No phone service MultipleLines_Yes InternetService_DSL InternetService_Fiber optic InternetService_No Contract_Month-to-month Contract_One year Contract_Two year PaymentMethod_Bank transfer (automatic) PaymentMethod_Credit card (automatic) PaymentMethod_Electronic check PaymentMethod_Mailed check tenure_cat_Tenure_0-12 tenure_cat_Tenure_12-24 tenure_cat_Tenure_24-48 tenure_cat_Tenure_48-60 tenure_cat_Tenure_gt-60 gender SeniorCitizen Partner Dependents PhoneService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies PaperlessBilling Churn
0 1 29.85 29.850000 0 1 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0
1 34 56.95 1889.500000 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0
2 2 53.85 108.150002 1 0 0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 1 1
3 45 42.30 1840.750000 0 1 0 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0
4 2 70.70 151.649994 1 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
7027 24 84.80 1990.500000 0 0 1 1 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0
7028 72 103.20 7362.899902 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0 1 1 0 1 1 1 0
7029 11 29.60 346.450012 0 1 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0
7030 4 74.40 306.600006 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 1 1
7031 66 105.65 6844.500000 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 1 1 1 1 1 0

7032 rows × 34 columns

In [71]:
# Scale numeric columns using StandardScalar()

scalar = StandardScaler().fit_transform(df[num_cols])
In [72]:
scalar
Out[72]:
array([[-1.28024804, -1.16169394, -0.99419409],
       [ 0.06430269, -0.26087792, -0.17373982],
       [-1.23950408, -0.36392329, -0.95964911],
       ...,
       [-0.87280842, -1.17000405, -0.85451413],
       [-1.15801615,  0.31916782, -0.87209545],
       [ 1.36810945,  1.35793167,  2.01234407]])
In [73]:
df_num = df[df.select_dtypes(exclude=['object']).keys()]
In [74]:
df_num.columns
Out[74]:
Index(['tenure', 'MonthlyCharges', 'TotalCharges'], dtype='object')
In [75]:
df_num = pd.DataFrame(StandardScaler().fit_transform(df_num))
#df = pd.DataFrame(scale.fit_transform(df.values), columns=df.columns, index=df.index)
In [76]:
df_num.rename(columns={0:'tenure_1',1:'MonthlyCharges_1',2:'TotalCharges_1'}, inplace=True)
#df.rename(columns={"A": "a", "B": "c"})
In [77]:
df_num
Out[77]:
tenure_1 MonthlyCharges_1 TotalCharges_1
0 -1.280248 -1.161694 -0.994194
1 0.064303 -0.260878 -0.173740
2 -1.239504 -0.363923 -0.959649
3 0.512486 -0.747850 -0.195248
4 -1.239504 0.196178 -0.940457
... ... ... ...
7027 -0.343137 0.664868 -0.129180
7028 1.612573 1.276493 2.241056
7029 -0.872808 -1.170004 -0.854514
7030 -1.158016 0.319168 -0.872095
7031 1.368109 1.357932 2.012344

7032 rows × 3 columns

In [78]:
# Drop Original values and Merge the scaled values with your dataframe.

df_final = pd.concat([df_1,df_num],axis=1)
In [79]:
df_final
Out[79]:
tenure MonthlyCharges TotalCharges MultipleLines_No MultipleLines_No phone service MultipleLines_Yes InternetService_DSL InternetService_Fiber optic InternetService_No Contract_Month-to-month Contract_One year Contract_Two year PaymentMethod_Bank transfer (automatic) PaymentMethod_Credit card (automatic) PaymentMethod_Electronic check PaymentMethod_Mailed check tenure_cat_Tenure_0-12 tenure_cat_Tenure_12-24 tenure_cat_Tenure_24-48 tenure_cat_Tenure_48-60 tenure_cat_Tenure_gt-60 gender SeniorCitizen Partner Dependents PhoneService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies PaperlessBilling Churn tenure_1 MonthlyCharges_1 TotalCharges_1
0 1 29.85 29.850000 0 1 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 -1.280248 -1.161694 -0.994194
1 34 56.95 1889.500000 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0.064303 -0.260878 -0.173740
2 2 53.85 108.150002 1 0 0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 1 1 -1.239504 -0.363923 -0.959649
3 45 42.30 1840.750000 0 1 0 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0 0.512486 -0.747850 -0.195248
4 2 70.70 151.649994 1 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 -1.239504 0.196178 -0.940457
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
7027 24 84.80 1990.500000 0 0 1 1 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 -0.343137 0.664868 -0.129180
7028 72 103.20 7362.899902 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0 1 1 0 1 1 1 0 1.612573 1.276493 2.241056
7029 11 29.60 346.450012 0 1 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0 -0.872808 -1.170004 -0.854514
7030 4 74.40 306.600006 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 1 1 -1.158016 0.319168 -0.872095
7031 66 105.65 6844.500000 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 1 1 1 1 1 0 1.368109 1.357932 2.012344

7032 rows × 37 columns

In [80]:
df_final.drop(['tenure','MonthlyCharges','TotalCharges'], axis=1, inplace=True)
In [81]:
df_final
Out[81]:
MultipleLines_No MultipleLines_No phone service MultipleLines_Yes InternetService_DSL InternetService_Fiber optic InternetService_No Contract_Month-to-month Contract_One year Contract_Two year PaymentMethod_Bank transfer (automatic) PaymentMethod_Credit card (automatic) PaymentMethod_Electronic check PaymentMethod_Mailed check tenure_cat_Tenure_0-12 tenure_cat_Tenure_12-24 tenure_cat_Tenure_24-48 tenure_cat_Tenure_48-60 tenure_cat_Tenure_gt-60 gender SeniorCitizen Partner Dependents PhoneService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies PaperlessBilling Churn tenure_1 MonthlyCharges_1 TotalCharges_1
0 0 1 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 -1.280248 -1.161694 -0.994194
1 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0.064303 -0.260878 -0.173740
2 1 0 0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 1 1 -1.239504 -0.363923 -0.959649
3 0 1 0 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0 0.512486 -0.747850 -0.195248
4 1 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 -1.239504 0.196178 -0.940457
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
7027 0 0 1 1 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 -0.343137 0.664868 -0.129180
7028 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0 1 1 0 1 1 1 0 1.612573 1.276493 2.241056
7029 0 1 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0 -0.872808 -1.170004 -0.854514
7030 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 1 1 -1.158016 0.319168 -0.872095
7031 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 1 1 1 1 1 0 1.368109 1.357932 2.012344

7032 rows × 34 columns

In [82]:
# Model Building

train,test = train_test_split(df_final,test_size = .25 ,random_state = 111)

cols    = [i for i in df_final.columns if i not in Id_col + target_col]
X_train = train[cols]
y_train = train[target_col]
X_test  = test[cols]
y_test  = test[target_col]
In [83]:
def churn_prediction(algorithm,x_train,x_test,y_train,y_test,cols,threshold_plot) :
    
    # Model
    algorithm.fit(x_train,y_train)
    # Prediction
    predictions   = algorithm.predict(x_test)
    # Confusion matrix
    confusion_mat = confusion_matrix(y_test,predictions)
    # Probabilities
    probabilities = algorithm.predict_proba(x_test)
    
    print(algorithm)
    print ("\n Classification report : \n",classification_report(y_test,predictions))
    print ("Accuracy   Score : ",accuracy_score(y_test,predictions))
    #roc_auc_score
    model_roc_auc = roc_auc_score(y_test,predictions) 
    print ("Area under curve : ",model_roc_auc,"\n")
    fpr,tpr,thresholds = roc_curve(y_test,probabilities[:,1])
    #print('fpr : ', fpr,'\n')
    #print('tpr : ', tpr,'\n')
    #print('thresholds : ', thresholds,'\n')
    
    if threshold_plot == True : 
        visualizer = DiscriminationThreshold(algorithm)
        visualizer.fit(X_train,y_train)
        visualizer.poof()
    roc_auc = auc(fpr, tpr)

    plt.figure()
    plt.plot(fpr, tpr, color='darkorange', lw=1, label='ROC curve (area = %0.2f)' % roc_auc)
    plt.plot([0, 1], [0, 1], color='navy', lw=1, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic')
    plt.legend(loc="lower right")
    plt.show()
      
    if algorithm == 'randomForest':
        print('Accuracy of random forest classifier on test set: {:.2f}'.format(randomForest.score(X_test, y_test)))
        # Utility function to report the test scores
In [84]:
def report(results, n_top=3):
    for i in range(1,n_top+1):
        candidates = np.flatnonzero(results['rank_test_score']==i)
        for candidate in candidates:
            print("Model with rank : {0}".format(i))
            print("Mean validation score : {0:.3f}(std: {1:.3f})".format(results['mean_test_score'][candidate],results['std_test_score'][candidate]))
            print("Parameters: {0}".format(results['params'][candidate]))
            print("")
In [85]:
print('\nRunning Logitic Regression \n')

logistic = LogisticRegression()
churn_prediction(logistic,X_train,X_test,y_train,y_test,cols,threshold_plot = True)
print('\n Logistixc Regression Finished\n')


print('\n Running Decision Tree Unpruned\n')
# Create each decision tree (pruned and unpruned)
decisionTree_unpruned = DecisionTreeClassifier()
decisionTree = DecisionTreeClassifier(max_depth = 4)

# Fit each tree to our training data
churn_prediction(decisionTree_unpruned, X_train,X_test,y_train,y_test,cols,threshold_plot=True)
#decisionTree_unpruned = decisionTree_unpruned.fit(X=train_x, y=train_y)
print('\nRunning Desicion Tree Pruned\n')
churn_prediction(decisionTree, X_train,X_test, y_train, y_test,cols,threshold_plot=True)
#decisionTree = decisionTree.fit(X=train_x, y=train_y)

print('\n Decision Tree Finished\n')

print('\n Running Random Forest \n')

randomForest = RandomForestClassifier()
churn_prediction(randomForest,X_train,X_test, y_train, y_test,cols,threshold_plot=True)
#randomForest.fit(train_x, train_y)

print('\nRandom Forest Finished\n')

Running Logitic Regression 

LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='warn', n_jobs=None, penalty='l2',
                   random_state=None, solver='warn', tol=0.0001, verbose=0,
                   warm_start=False)

 Classification report : 
               precision    recall  f1-score   support

           0       0.83      0.91      0.87      1268
           1       0.69      0.53      0.60       490

    accuracy                           0.80      1758
   macro avg       0.76      0.72      0.73      1758
weighted avg       0.79      0.80      0.79      1758

Accuracy   Score :  0.8026166097838453
Area under curve :  0.7185443893645785 


 Logistixc Regression Finished


 Running Decision Tree Unpruned

DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
                       max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, presort=False,
                       random_state=None, splitter='best')

 Classification report : 
               precision    recall  f1-score   support

           0       0.81      0.82      0.82      1268
           1       0.52      0.49      0.50       490

    accuracy                           0.73      1758
   macro avg       0.66      0.66      0.66      1758
weighted avg       0.73      0.73      0.73      1758

Accuracy   Score :  0.7309442548350398
Area under curve :  0.6575902916371595 


Running Desicion Tree Pruned

DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=4,
                       max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, presort=False,
                       random_state=None, splitter='best')

 Classification report : 
               precision    recall  f1-score   support

           0       0.79      0.94      0.86      1268
           1       0.72      0.37      0.48       490

    accuracy                           0.78      1758
   macro avg       0.75      0.65      0.67      1758
weighted avg       0.77      0.78      0.76      1758

Accuracy   Score :  0.782707622298066
Area under curve :  0.6546562157986223 


 Decision Tree Finished


 Running Random Forest 

RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
                       max_depth=None, max_features='auto', max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=10,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False)

 Classification report : 
               precision    recall  f1-score   support

           0       0.80      0.92      0.86      1268
           1       0.67      0.42      0.52       490

    accuracy                           0.78      1758
   macro avg       0.74      0.67      0.69      1758
weighted avg       0.77      0.78      0.76      1758

Accuracy   Score :  0.7798634812286689
Area under curve :  0.6695889396768171 


Random Forest Finished
In [86]:
#Specify parameters and distributions to sample from
param_dist = {'max_depth': [3,None],
'max_features' : sp_randint(1,11),
'min_samples_split' : sp_randint(2,11),
'bootstrap' :  [True, False],
'criterion' : ['gini','entropy']}

# Run randomized search
n_iter_search = 20
random_search = RandomizedSearchCV(randomForest, param_distributions=param_dist,n_iter=n_iter_search, cv=5)
start = time()
random_search.fit(X_train, y_train)
print("Randomized Search took %.2f seconds for %d candidates " "parameter settings." % ((time()-start),n_iter_search))
report(random_search.cv_results_)

Randomized Search took 4.41 seconds for 20 candidates parameter settings.
Model with rank : 1
Mean validation score : 0.791(std: 0.009)
Parameters: {'bootstrap': True, 'criterion': 'gini', 'max_depth': None, 'max_features': 10, 'min_samples_split': 9}

Model with rank : 2
Mean validation score : 0.789(std: 0.008)
Parameters: {'bootstrap': False, 'criterion': 'entropy', 'max_depth': 3, 'max_features': 7, 'min_samples_split': 6}

Model with rank : 3
Mean validation score : 0.788(std: 0.011)
Parameters: {'bootstrap': False, 'criterion': 'entropy', 'max_depth': 3, 'max_features': 7, 'min_samples_split': 7}
In [87]:
# We take the first one "Model with rank : 1" and check if the model has better performance

random_rf = RandomForestClassifier(bootstrap= False, 
                                   criterion='entropy',  
                                   max_features= 5, min_samples_split= 10)
churn_prediction(random_rf, X_train, X_test,y_train,y_test,cols,threshold_plot=True)

RandomForestClassifier(bootstrap=False, class_weight=None, criterion='entropy',
                       max_depth=None, max_features=5, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=10,
                       min_weight_fraction_leaf=0.0, n_estimators=10,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False)

 Classification report : 
               precision    recall  f1-score   support

           0       0.82      0.90      0.86      1268
           1       0.65      0.48      0.56       490

    accuracy                           0.78      1758
   macro avg       0.73      0.69      0.71      1758
weighted avg       0.77      0.78      0.77      1758

Accuracy   Score :  0.7838452787258248
Area under curve :  0.6917578703405652
In [88]:
# Now we apply Grid Search

# Now use a full grid oerall parameters
param_grid = {"max_depth": [3, None],
             "max_features": [1,3,10],
             "min_samples_split":[2,3,10],
             "bootstrap":[True, False],
             "criterion":["gini","entropy"]}

# Run grid search
grid_search = GridSearchCV(random_rf, param_grid=param_grid, cv=5)
start = time()
grid_search.fit(X_train, y_train)

print("Grid Searchtook %.2f seconds for %d candidate parameter settings" % (time()-start, len(grid_search.cv_results_['params'])))
report(grid_search.cv_results_)

Grid Searchtook 16.61 seconds for 72 candidate parameter settings
Model with rank : 1
Mean validation score : 0.799(std: 0.007)
Parameters: {'bootstrap': True, 'criterion': 'gini', 'max_depth': None, 'max_features': 3, 'min_samples_split': 10}

Model with rank : 2
Mean validation score : 0.794(std: 0.004)
Parameters: {'bootstrap': True, 'criterion': 'entropy', 'max_depth': None, 'max_features': 3, 'min_samples_split': 10}

Model with rank : 3
Mean validation score : 0.792(std: 0.011)
Parameters: {'bootstrap': True, 'criterion': 'entropy', 'max_depth': None, 'max_features': 1, 'min_samples_split': 10}
In [89]:
# We take the second one "Model with rank : 2". As the Mean validation score is
# same for all so we pick the with the less Standard Deviation.

random_gs = RandomForestClassifier(bootstrap= True, criterion= 'entropy',
                                   max_features=10, min_samples_split=10)

churn_prediction(random_gs,X_train,X_test, y_train,y_test,cols, threshold_plot=True)

RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',
                       max_depth=None, max_features=10, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=10,
                       min_weight_fraction_leaf=0.0, n_estimators=10,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False)

 Classification report : 
               precision    recall  f1-score   support

           0       0.82      0.90      0.85      1268
           1       0.64      0.48      0.55       490

    accuracy                           0.78      1758
   macro avg       0.73      0.69      0.70      1758
weighted avg       0.77      0.78      0.77      1758

Accuracy   Score :  0.7798634812286689
Area under curve :  0.6864932723878194
In [90]:
# As we can see that the accuracy for the logistic regression is the highest, i.e. 80%
# But if we see there is a problem of class imbalance

df_final['Churn'].value_counts()
Out[90]:
0    5163
1    1869
Name: Churn, dtype: int64
In [91]:
# Calculate the model performance of each model.

from sklearn.metrics import f1_score

#gives model report in dataframe
def model_report(model,X_train,X_test,y_train,y_test,name) :
    model.fit(X_train,y_train)
    predictions  = model.predict(X_test)
    accuracy     = accuracy_score(y_test,predictions)
    recallscore  = recall_score(y_test,predictions)
    precision    = precision_score(y_test,predictions)
    roc_auc      = roc_auc_score(y_test,predictions)
    f1score      = f1_score(y_test,predictions) 
    
    
    df_model_perf = pd.DataFrame({'Model'           : [name],
                                  'Accuracy_score'  : [accuracy],
                                  'Recall_score'    : [recallscore],
                                  'Precision'       : [precision],
                                  'f1_score'        : [f1score],
                                  'Area_under_curve': [roc_auc],
                               })
    return df_model_perf

#outputs for every model
model1 = model_report(logistic,X_train,X_test,y_train,y_test,
                      "Logistic Regression")
model2 = model_report(decisionTree_unpruned,X_train,X_test,y_train,y_test,
                      "Decision Tree Unpruned")
model3 = model_report(decisionTree_unpruned,X_train,X_test,y_train,y_test,
                      "Decision Tree Pruned")
model4 = model_report(randomForest,X_train,X_test,y_train,y_test,
                      "Random Forest Classifier")
model5 = model_report(random_rf,X_train,X_test,y_train,y_test,
                      "Random Forest Using<br>RandomizedSearchCV")
model6 = model_report(random_gs,X_train,X_test,y_train,y_test,
                      "Random Forest Usnig<br>GridSearchCV")

#concat all models
model_performances = pd.concat([model1,model2,model3,
                                model4,model5,model6,],axis = 0).reset_index()

model_performances = model_performances.drop(columns = "index",axis =1)

table  = ff.create_table(np.round(model_performances,4))

py.iplot(table)